Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations5 883
Missing cells12 352
Missing cells (%)11.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 MiB
Average record size in memory738.0 B

Variable types

Categorical5
Numeric8
Text5
DateTime1

Alerts

Category is highly overall correlated with Instrument and 3 other fieldsHigh correlation
DEC is highly overall correlated with Instrument and 2 other fieldsHigh correlation
Exptime is highly overall correlated with Type and 2 other fieldsHigh correlation
Instrument is highly overall correlated with Category and 8 other fieldsHigh correlation
MJD-OBS is highly overall correlated with Instrument and 4 other fieldsHigh correlation
Mode is highly overall correlated with Category and 7 other fieldsHigh correlation
OBJECT is highly overall correlated with Category and 7 other fieldsHigh correlation
RA is highly overall correlated with Instrument and 4 other fieldsHigh correlation
Type is highly overall correlated with Category and 6 other fieldsHigh correlation
filter_lambda_max is highly overall correlated with DEC and 5 other fieldsHigh correlation
filter_lambda_min is highly overall correlated with DEC and 5 other fieldsHigh correlation
TPL START has 223 (3.8%) missing values Missing
filter_lambda_min has 4764 (81.0%) missing values Missing
filter_lambda_max has 4764 (81.0%) missing values Missing
Filter has 1171 (19.9%) missing values Missing
Airmass has 679 (11.5%) missing values Missing
DIMM Seeing at Start has 679 (11.5%) missing values Missing
Dataset ID has unique values Unique
Exptime has 205 (3.5%) zeros Zeros

Reproduction

Analysis started2025-04-05 13:36:03.827674
Analysis finished2025-04-05 13:36:13.341881
Duration9.51 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

OBJECT
Categorical

High correlation 

Distinct48
Distinct (%)0.8%
Missing8
Missing (%)0.1%
Memory size333.3 KiB
PROXIMA-CEN
1256 
PROX CENTAURI
571 
GJ551
549 
PROXIMA
442 
GL551
436 
Other values (43)
2621 

Length

Max length25
Median length16
Mean length8.9913191
Min length1

Characters and Unicode

Total characters52 824
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowOBJECT
2nd rowOBJECT
3rd rowOBJECT
4th rowOBJECT
5th rowOBJECT

Common Values

ValueCountFrequency (%)
PROXIMA-CEN 1256
21.3%
PROX CENTAURI 571
9.7%
GJ551 549
 
9.3%
PROXIMA 442
 
7.5%
GL551 436
 
7.4%
OBJECT 399
 
6.8%
PROXIMA CENTAURI 221
 
3.8%
STD 204
 
3.5%
V V645 CEN 192
 
3.3%
HIP_70890 186
 
3.2%
Other values (38) 1419
24.1%

Length

2025-04-05T14:36:13.453481image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
proxima-cen 1256
17.0%
proxima 898
12.2%
centauri 881
11.9%
prox 571
 
7.7%
gj551 549
 
7.4%
gl551 436
 
5.9%
object 399
 
5.4%
cen 340
 
4.6%
std 204
 
2.8%
v 192
 
2.6%
Other values (40) 1649
22.4%

Most occurring characters

ValueCountFrequency (%)
R 4151
 
7.9%
O 3920
 
7.4%
A 3710
 
7.0%
E 3630
 
6.9%
I 3559
 
6.7%
P 3543
 
6.7%
C 3332
 
6.3%
X 3022
 
5.7%
N 2963
 
5.6%
5 2920
 
5.5%
Other values (27) 18074
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 4151
 
7.9%
O 3920
 
7.4%
A 3710
 
7.0%
E 3630
 
6.9%
I 3559
 
6.7%
P 3543
 
6.7%
C 3332
 
6.3%
X 3022
 
5.7%
N 2963
 
5.6%
5 2920
 
5.5%
Other values (27) 18074
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 4151
 
7.9%
O 3920
 
7.4%
A 3710
 
7.0%
E 3630
 
6.9%
I 3559
 
6.7%
P 3543
 
6.7%
C 3332
 
6.3%
X 3022
 
5.7%
N 2963
 
5.6%
5 2920
 
5.5%
Other values (27) 18074
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 4151
 
7.9%
O 3920
 
7.4%
A 3710
 
7.0%
E 3630
 
6.9%
I 3559
 
6.7%
P 3543
 
6.7%
C 3332
 
6.3%
X 3022
 
5.7%
N 2963
 
5.6%
5 2920
 
5.5%
Other values (27) 18074
34.2%

RA
Real number (ℝ)

High correlation 

Distinct926
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.38737
Minimum217.25213
Maximum217.44887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2025-04-05T14:36:13.668569image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum217.25213
5-th percentile217.25296
Q1217.38671
median217.40508
Q3217.40896
95-th percentile217.42613
Maximum217.44887
Range0.19675
Interquartile range (IQR)0.02225

Descriptive statistics

Standard deviation0.047167175
Coefficient of variation (CV)0.00021697294
Kurtosis3.6611185
Mean217.38737
Median Absolute Deviation (MAD)0.013416
Skewness-2.1959888
Sum1278889.9
Variance0.0022247424
MonotonicityIncreasing
2025-04-05T14:36:13.840156image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
217.408292 504
 
8.6%
217.408625 400
 
6.8%
217.252958 356
 
6.1%
217.408958 328
 
5.6%
217.253083 217
 
3.7%
217.423208 128
 
2.2%
217.389208 87
 
1.5%
217.389333 69
 
1.2%
217.408375 64
 
1.1%
217.423292 63
 
1.1%
Other values (916) 3667
62.3%
ValueCountFrequency (%)
217.252125 1
 
< 0.1%
217.252375 2
 
< 0.1%
217.25275 1
 
< 0.1%
217.252833 1
 
< 0.1%
217.252958 356
6.1%
217.253083 217
3.7%
217.253542 1
 
< 0.1%
217.255583 8
 
0.1%
217.261667 1
 
< 0.1%
217.361833 1
 
< 0.1%
ValueCountFrequency (%)
217.448875 1
< 0.1%
217.439042 1
< 0.1%
217.436458 1
< 0.1%
217.435 2
< 0.1%
217.434042 1
< 0.1%
217.433875 1
< 0.1%
217.433708 1
< 0.1%
217.433292 1
< 0.1%
217.433208 1
< 0.1%
217.433125 1
< 0.1%

DEC
Real number (ℝ)

High correlation 

Distinct369
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-62.678826
Minimum-62.698194
Maximum-62.646083
Zeros0
Zeros (%)0.0%
Negative5883
Negative (%)100.0%
Memory size46.1 KiB
2025-04-05T14:36:14.024472image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum-62.698194
5-th percentile-62.695278
Q1-62.678444
median-62.677583
Q3-62.67575
95-th percentile-62.674111
Maximum-62.646083
Range0.052111
Interquartile range (IQR)0.002694

Descriptive statistics

Standard deviation0.0060769094
Coefficient of variation (CV)-9.6953147 × 10-5
Kurtosis3.4085914
Mean-62.678826
Median Absolute Deviation (MAD)0.001473
Skewness-1.7967117
Sum-368739.53
Variance3.6928828 × 10-5
MonotonicityNot monotonic
2025-04-05T14:36:14.177428image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-62.677583 502
 
8.5%
-62.677639 408
 
6.9%
-62.695278 356
 
6.1%
-62.677667 331
 
5.6%
-62.695333 217
 
3.7%
-62.675972 98
 
1.7%
-62.676083 93
 
1.6%
-62.677806 91
 
1.5%
-62.67625 89
 
1.5%
-62.675639 77
 
1.3%
Other values (359) 3621
61.6%
ValueCountFrequency (%)
-62.698194 1
 
< 0.1%
-62.697833 1
 
< 0.1%
-62.695917 8
 
0.1%
-62.695417 1
 
< 0.1%
-62.695333 217
3.7%
-62.695278 356
6.1%
-62.695222 2
 
< 0.1%
-62.695167 2
 
< 0.1%
-62.695083 1
 
< 0.1%
-62.693139 1
 
< 0.1%
ValueCountFrequency (%)
-62.646083 2
< 0.1%
-62.651667 2
< 0.1%
-62.657222 2
< 0.1%
-62.65725 4
0.1%
-62.663917 2
< 0.1%
-62.663972 2
< 0.1%
-62.664028 2
< 0.1%
-62.66425 2
< 0.1%
-62.664889 2
< 0.1%
-62.665778 2
< 0.1%
Distinct145
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size355.7 KiB
2025-04-05T14:36:14.518873image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length14
Median length13
Mean length12.889682
Min length12

Characters and Unicode

Total characters75 830
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.3%

Sample

1st row299.C-5022(A)
2nd row299.C-5022(A)
3rd row299.C-5022(A)
4th row299.C-5022(A)
5th row60.A-9800(L)
ValueCountFrequency (%)
082.d-0953(a 1260
21.4%
299.c-5022(a 583
 
9.9%
191.c-0505(a 216
 
3.7%
69.c-0298(a 188
 
3.2%
099.c-0127(a 171
 
2.9%
71.c-0388(a 128
 
2.2%
182.c-0748(a 115
 
2.0%
113.26dt.001 104
 
1.8%
182.c-0748(d 104
 
1.8%
1100.c-0481(f 103
 
1.8%
Other values (135) 2911
49.5%
2025-04-05T14:36:15.043020image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11049
14.6%
. 6336
 
8.4%
2 5708
 
7.5%
( 5430
 
7.2%
- 5430
 
7.2%
) 5430
 
7.2%
9 5227
 
6.9%
1 4315
 
5.7%
A 4083
 
5.4%
8 3938
 
5.2%
Other values (27) 18884
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75830
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11049
14.6%
. 6336
 
8.4%
2 5708
 
7.5%
( 5430
 
7.2%
- 5430
 
7.2%
) 5430
 
7.2%
9 5227
 
6.9%
1 4315
 
5.7%
A 4083
 
5.4%
8 3938
 
5.2%
Other values (27) 18884
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75830
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11049
14.6%
. 6336
 
8.4%
2 5708
 
7.5%
( 5430
 
7.2%
- 5430
 
7.2%
) 5430
 
7.2%
9 5227
 
6.9%
1 4315
 
5.7%
A 4083
 
5.4%
8 3938
 
5.2%
Other values (27) 18884
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75830
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11049
14.6%
. 6336
 
8.4%
2 5708
 
7.5%
( 5430
 
7.2%
- 5430
 
7.2%
) 5430
 
7.2%
9 5227
 
6.9%
1 4315
 
5.7%
A 4083
 
5.4%
8 3938
 
5.2%
Other values (27) 18884
24.9%

Instrument
Categorical

High correlation 

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
UVES
1751 
CRIRES
882 
SPHERE
876 
HARPS
605 
HAWKI
591 
Other values (15)
1178 

Length

Max length11
Median length9
Mean length5.391297
Min length4

Characters and Unicode

Total characters31 717
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHAWKI
2nd rowHAWKI
3rd rowHAWKI
4th rowHAWKI
5th rowHAWKI

Common Values

ValueCountFrequency (%)
UVES 1751
29.8%
CRIRES 882
15.0%
SPHERE 876
14.9%
HARPS 605
 
10.3%
HAWKI 591
 
10.0%
NAOS+CONICA 247
 
4.2%
ISAAC 188
 
3.2%
PIONIER 152
 
2.6%
AMBER 135
 
2.3%
ESPRESSO 120
 
2.0%
Other values (10) 336
 
5.7%

Length

2025-04-05T14:36:15.214285image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
uves 1751
29.8%
crires 882
15.0%
sphere 876
14.9%
harps 605
 
10.3%
hawki 591
 
10.0%
naos+conica 247
 
4.2%
isaac 188
 
3.2%
pionier 152
 
2.6%
amber 135
 
2.3%
espresso 120
 
2.0%
Other values (10) 336
 
5.7%

Most occurring characters

ValueCountFrequency (%)
S 5166
16.3%
E 5000
15.8%
R 3804
12.0%
I 2452
7.7%
A 2272
7.2%
H 2125
6.7%
V 1800
 
5.7%
P 1790
 
5.6%
U 1751
 
5.5%
C 1602
 
5.1%
Other values (17) 3955
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31717
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 5166
16.3%
E 5000
15.8%
R 3804
12.0%
I 2452
7.7%
A 2272
7.2%
H 2125
6.7%
V 1800
 
5.7%
P 1790
 
5.6%
U 1751
 
5.5%
C 1602
 
5.1%
Other values (17) 3955
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31717
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 5166
16.3%
E 5000
15.8%
R 3804
12.0%
I 2452
7.7%
A 2272
7.2%
H 2125
6.7%
V 1800
 
5.7%
P 1790
 
5.6%
U 1751
 
5.5%
C 1602
 
5.1%
Other values (17) 3955
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31717
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 5166
16.3%
E 5000
15.8%
R 3804
12.0%
I 2452
7.7%
A 2272
7.2%
H 2125
6.7%
V 1800
 
5.7%
P 1790
 
5.6%
U 1751
 
5.5%
C 1602
 
5.1%
Other values (17) 3955
12.5%

Category
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size325.3 KiB
SCIENCE
4295 
ACQUISITION
1114 
CALIB
474 

Length

Max length11
Median length7
Mean length7.5962944
Min length5

Characters and Unicode

Total characters44 689
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACQUISITION
2nd rowACQUISITION
3rd rowACQUISITION
4th rowACQUISITION
5th rowACQUISITION

Common Values

ValueCountFrequency (%)
SCIENCE 4295
73.0%
ACQUISITION 1114
 
18.9%
CALIB 474
 
8.1%

Length

2025-04-05T14:36:15.340858image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-05T14:36:15.433958image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
science 4295
73.0%
acquisition 1114
 
18.9%
calib 474
 
8.1%

Most occurring characters

ValueCountFrequency (%)
C 10178
22.8%
E 8590
19.2%
I 8111
18.1%
S 5409
12.1%
N 5409
12.1%
A 1588
 
3.6%
Q 1114
 
2.5%
U 1114
 
2.5%
T 1114
 
2.5%
O 1114
 
2.5%
Other values (2) 948
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44689
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 10178
22.8%
E 8590
19.2%
I 8111
18.1%
S 5409
12.1%
N 5409
12.1%
A 1588
 
3.6%
Q 1114
 
2.5%
U 1114
 
2.5%
T 1114
 
2.5%
O 1114
 
2.5%
Other values (2) 948
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44689
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 10178
22.8%
E 8590
19.2%
I 8111
18.1%
S 5409
12.1%
N 5409
12.1%
A 1588
 
3.6%
Q 1114
 
2.5%
U 1114
 
2.5%
T 1114
 
2.5%
O 1114
 
2.5%
Other values (2) 948
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44689
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 10178
22.8%
E 8590
19.2%
I 8111
18.1%
S 5409
12.1%
N 5409
12.1%
A 1588
 
3.6%
Q 1114
 
2.5%
U 1114
 
2.5%
T 1114
 
2.5%
O 1114
 
2.5%
Other values (2) 948
 
2.1%

Type
Categorical

High correlation 

Distinct45
Distinct (%)0.8%
Missing26
Missing (%)0.4%
Memory size326.9 KiB
OBJECT
2048 
SLIT
874 
OBJECT,POINT
752 
SKY
220 
OBJECT,CENTER
218 
Other values (40)
1745 

Length

Max length18
Median length15
Mean length7.8811678
Min length3

Characters and Unicode

Total characters46 160
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOBJECT
2nd rowOBJECT
3rd rowOBJECT
4th rowOBJECT
5th rowOBJECT

Common Values

ValueCountFrequency (%)
OBJECT 2048
34.8%
SLIT 874
14.9%
OBJECT,POINT 752
 
12.8%
SKY 220
 
3.7%
OBJECT,CENTER 218
 
3.7%
STD 206
 
3.5%
OBJECT,AO 185
 
3.1%
STAR,WAVE,M5 168
 
2.9%
OBJECT,FP 135
 
2.3%
LAMP,METROLOGY 128
 
2.2%
Other values (35) 923
15.7%

Length

2025-04-05T14:36:15.606414image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
object 2048
35.0%
slit 874
14.9%
object,point 752
 
12.8%
sky 220
 
3.8%
object,center 218
 
3.7%
std 206
 
3.5%
object,ao 185
 
3.2%
star,wave,m5 168
 
2.9%
object,fp 135
 
2.3%
lamp,metrology 128
 
2.2%
Other values (35) 923
15.8%

Most occurring characters

ValueCountFrequency (%)
T 6469
14.0%
O 5107
11.1%
E 4806
10.4%
C 4000
8.7%
B 3601
 
7.8%
J 3589
 
7.8%
, 3000
 
6.5%
S 2047
 
4.4%
I 1918
 
4.2%
A 1626
 
3.5%
Other values (21) 9997
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 6469
14.0%
O 5107
11.1%
E 4806
10.4%
C 4000
8.7%
B 3601
 
7.8%
J 3589
 
7.8%
, 3000
 
6.5%
S 2047
 
4.4%
I 1918
 
4.2%
A 1626
 
3.5%
Other values (21) 9997
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 6469
14.0%
O 5107
11.1%
E 4806
10.4%
C 4000
8.7%
B 3601
 
7.8%
J 3589
 
7.8%
, 3000
 
6.5%
S 2047
 
4.4%
I 1918
 
4.2%
A 1626
 
3.5%
Other values (21) 9997
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 6469
14.0%
O 5107
11.1%
E 4806
10.4%
C 4000
8.7%
B 3601
 
7.8%
J 3589
 
7.8%
, 3000
 
6.5%
S 2047
 
4.4%
I 1918
 
4.2%
A 1626
 
3.5%
Other values (21) 9997
21.7%

Mode
Categorical

High correlation 

Distinct26
Distinct (%)0.4%
Missing8
Missing (%)0.1%
Memory size345.4 KiB
IMAGE
1356 
ECHELLE
1330 
IMAGE,HIT
571 
SPECTRUM,NODDING,OTHER
441 
SPECTRUM
417 
Other values (21)
1760 

Length

Max length30
Median length28
Mean length11.100766
Min length3

Characters and Unicode

Total characters65 217
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIMAGE
2nd rowIMAGE
3rd rowIMAGE
4th rowIMAGE
5th rowIMAGE

Common Values

ValueCountFrequency (%)
IMAGE 1356
23.0%
ECHELLE 1330
22.6%
IMAGE,HIT 571
9.7%
SPECTRUM,NODDING,OTHER 441
 
7.5%
SPECTRUM 417
 
7.1%
INTERFEROMETRY 295
 
5.0%
ECHELLE,ABSORPTION-CELL,SLIC#3 239
 
4.1%
IFU 229
 
3.9%
IMAGE,DUAL,CORONOGRAPHY 189
 
3.2%
CORONOGRAPHY 111
 
1.9%
Other values (16) 697
11.8%

Length

2025-04-05T14:36:15.760225image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
image 1356
23.1%
echelle 1330
22.6%
image,hit 571
9.7%
spectrum,nodding,other 441
 
7.5%
spectrum 417
 
7.1%
interferometry 295
 
5.0%
echelle,absorption-cell,slic#3 239
 
4.1%
ifu 229
 
3.9%
image,dual,coronography 189
 
3.2%
coronography 111
 
1.9%
Other values (16) 697
11.9%

Most occurring characters

ValueCountFrequency (%)
E 10607
16.3%
I 5172
 
7.9%
L 4880
 
7.5%
R 4322
 
6.6%
M 3882
 
6.0%
C 3838
 
5.9%
T 3697
 
5.7%
O 3581
 
5.5%
A 3509
 
5.4%
, 3412
 
5.2%
Other values (17) 18317
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65217
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 10607
16.3%
I 5172
 
7.9%
L 4880
 
7.5%
R 4322
 
6.6%
M 3882
 
6.0%
C 3838
 
5.9%
T 3697
 
5.7%
O 3581
 
5.5%
A 3509
 
5.4%
, 3412
 
5.2%
Other values (17) 18317
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65217
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 10607
16.3%
I 5172
 
7.9%
L 4880
 
7.5%
R 4322
 
6.6%
M 3882
 
6.0%
C 3838
 
5.9%
T 3697
 
5.7%
O 3581
 
5.5%
A 3509
 
5.4%
, 3412
 
5.2%
Other values (17) 18317
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65217
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 10607
16.3%
I 5172
 
7.9%
L 4880
 
7.5%
R 4322
 
6.6%
M 3882
 
6.0%
C 3838
 
5.9%
T 3697
 
5.7%
O 3581
 
5.5%
A 3509
 
5.4%
, 3412
 
5.2%
Other values (17) 18317
28.1%

Dataset ID
Text

Unique 

Distinct5883
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size446.2 KiB
2025-04-05T14:36:16.000486image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length29
Median length29
Mean length28.64066
Min length28

Characters and Unicode

Total characters168 493
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 883 ?
Unique (%)100.0%

Sample

1st rowHAWKI.2017-06-28T03:56:21.604
2nd rowHAWKI.2017-06-28T01:47:30.092
3rd rowHAWKI.2017-06-28T01:50:10.810
4th rowHAWKI.2017-06-28T03:58:13.314
5th rowHAWKI.2017-05-27T22:52:04.495
ValueCountFrequency (%)
hawki.2017-06-28t05:20:14.297 1
 
< 0.1%
crire.2011-04-30t04:29:26.530 1
 
< 0.1%
hawki.2017-06-28t03:56:21.604 1
 
< 0.1%
hawki.2017-06-28t01:47:30.092 1
 
< 0.1%
hawki.2017-06-28t01:50:10.810 1
 
< 0.1%
hawki.2017-06-28t03:58:13.314 1
 
< 0.1%
hawki.2017-05-27t22:52:04.495 1
 
< 0.1%
hawki.2017-06-28t05:15:11.757 1
 
< 0.1%
hawki.2017-06-28t05:21:14.741 1
 
< 0.1%
hawki.2017-06-28t05:22:45.633 1
 
< 0.1%
Other values (5873) 5873
99.8%
2025-04-05T14:36:16.368430image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 27844
16.5%
2 15926
 
9.5%
: 11766
 
7.0%
. 11766
 
7.0%
- 11766
 
7.0%
1 11420
 
6.8%
3 8450
 
5.0%
5 7221
 
4.3%
4 7141
 
4.2%
9 5935
 
3.5%
Other values (23) 49258
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168493
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 27844
16.5%
2 15926
 
9.5%
: 11766
 
7.0%
. 11766
 
7.0%
- 11766
 
7.0%
1 11420
 
6.8%
3 8450
 
5.0%
5 7221
 
4.3%
4 7141
 
4.2%
9 5935
 
3.5%
Other values (23) 49258
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168493
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 27844
16.5%
2 15926
 
9.5%
: 11766
 
7.0%
. 11766
 
7.0%
- 11766
 
7.0%
1 11420
 
6.8%
3 8450
 
5.0%
5 7221
 
4.3%
4 7141
 
4.2%
9 5935
 
3.5%
Other values (23) 49258
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168493
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 27844
16.5%
2 15926
 
9.5%
: 11766
 
7.0%
. 11766
 
7.0%
- 11766
 
7.0%
1 11420
 
6.8%
3 8450
 
5.0%
5 7221
 
4.3%
4 7141
 
4.2%
9 5935
 
3.5%
Other values (23) 49258
29.2%
Distinct395
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size46.1 KiB
Minimum2000-03-29 00:00:00
Maximum2026-02-14 00:00:00
2025-04-05T14:36:16.560637image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:16.735699image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TPL ID
Text

Distinct60
Distinct (%)1.0%
Missing30
Missing (%)0.5%
Memory size401.8 KiB
2025-04-05T14:36:17.083888image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length30
Median length28
Mean length21.109175
Min length15

Characters and Unicode

Total characters123 552
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowHAWKI_img_acq_FastPhot
2nd rowHAWKI_img_acq_FastPhot
3rd rowHAWKI_img_acq_FastPhot
4th rowHAWKI_img_acq_FastPhot
5th rowHAWKI_img_acq_FastPhot
ValueCountFrequency (%)
uves_dic2_obs_expfree 1258
21.5%
sphere_irdifs_obs 620
10.6%
hawki_img_obs_fastphot 571
9.8%
crires_spec_obs_autonodonslit 524
 
9.0%
harps_ech_obs_all 479
 
8.2%
uves_red_obs_exp 284
 
4.9%
uves_red_obs_expfree 205
 
3.5%
isaacsw_spec_cal_standardstar 186
 
3.2%
crires_pol_obs_autonodonslit 179
 
3.1%
amber_3tstd_obs_1row 135
 
2.3%
Other values (50) 1412
24.1%
2025-04-05T14:36:17.584403image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 16596
 
13.4%
o 8420
 
6.8%
s 8126
 
6.6%
e 7603
 
6.2%
S 6596
 
5.3%
b 5265
 
4.3%
E 5005
 
4.1%
i 4925
 
4.0%
R 3937
 
3.2%
d 3797
 
3.1%
Other values (43) 53282
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 16596
 
13.4%
o 8420
 
6.8%
s 8126
 
6.6%
e 7603
 
6.2%
S 6596
 
5.3%
b 5265
 
4.3%
E 5005
 
4.1%
i 4925
 
4.0%
R 3937
 
3.2%
d 3797
 
3.1%
Other values (43) 53282
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 16596
 
13.4%
o 8420
 
6.8%
s 8126
 
6.6%
e 7603
 
6.2%
S 6596
 
5.3%
b 5265
 
4.3%
E 5005
 
4.1%
i 4925
 
4.0%
R 3937
 
3.2%
d 3797
 
3.1%
Other values (43) 53282
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 16596
 
13.4%
o 8420
 
6.8%
s 8126
 
6.6%
e 7603
 
6.2%
S 6596
 
5.3%
b 5265
 
4.3%
E 5005
 
4.1%
i 4925
 
4.0%
R 3937
 
3.2%
d 3797
 
3.1%
Other values (43) 53282
43.1%

TPL START
Text

Missing 

Distinct967
Distinct (%)17.1%
Missing223
Missing (%)3.8%
Memory size382.7 KiB
2025-04-05T14:36:17.857085image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length19
Median length19
Mean length18.949117
Min length1

Characters and Unicode

Total characters107 252
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique502 ?
Unique (%)8.9%

Sample

1st row2017-06-28T03:51:02
2nd row2017-06-28T01:34:24
3rd row2017-06-28T01:34:24
4th row2017-06-28T03:51:02
5th row2017-05-27T22:37:58
ValueCountFrequency (%)
2009-03-10t02:30:06 488
 
8.6%
2009-03-14t02:07:08 388
 
6.9%
2017-06-28t04:01:55 355
 
6.3%
2009-03-12t02:12:17 316
 
5.6%
2017-06-28t01:54:34 216
 
3.8%
2002-08-05t00:29:37 120
 
2.1%
2003-07-20t02:37:21 100
 
1.8%
2009-03-12t09:08:51 60
 
1.1%
2002-08-04t02:57:29 51
 
0.9%
2017-03-20t05:26:03 42
 
0.7%
Other values (956) 3508
62.2%
2025-04-05T14:36:18.309000image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26344
24.6%
2 14252
13.3%
- 11288
10.5%
: 11288
10.5%
1 9230
 
8.6%
3 5924
 
5.5%
T 5644
 
5.3%
5 4817
 
4.5%
4 4646
 
4.3%
7 3740
 
3.5%
Other values (4) 10079
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 107252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 26344
24.6%
2 14252
13.3%
- 11288
10.5%
: 11288
10.5%
1 9230
 
8.6%
3 5924
 
5.5%
T 5644
 
5.3%
5 4817
 
4.5%
4 4646
 
4.3%
7 3740
 
3.5%
Other values (4) 10079
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 107252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 26344
24.6%
2 14252
13.3%
- 11288
10.5%
: 11288
10.5%
1 9230
 
8.6%
3 5924
 
5.5%
T 5644
 
5.3%
5 4817
 
4.5%
4 4646
 
4.3%
7 3740
 
3.5%
Other values (4) 10079
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 107252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 26344
24.6%
2 14252
13.3%
- 11288
10.5%
: 11288
10.5%
1 9230
 
8.6%
3 5924
 
5.5%
T 5644
 
5.3%
5 4817
 
4.5%
4 4646
 
4.3%
7 3740
 
3.5%
Other values (4) 10079
 
9.4%

Exptime
Real number (ℝ)

High correlation  Zeros 

Distinct230
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.58459
Minimum0
Maximum3120.002
Zeros205
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2025-04-05T14:36:18.485137image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q11.429
median28.407
Q399.997
95-th percentile1000.001
Maximum3120.002
Range3120.002
Interquartile range (IQR)98.568

Descriptive statistics

Standard deviation366.76114
Coefficient of variation (CV)1.9869543
Kurtosis5.4857488
Mean184.58459
Median Absolute Deviation (MAD)28.209
Skewness2.4019301
Sum1085911.1
Variance134513.73
MonotonicityNot monotonic
2025-04-05T14:36:18.668569image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.407 571
 
9.7%
20 440
 
7.5%
99.997 354
 
6.0%
0.098 317
 
5.4%
0.198 290
 
4.9%
60 226
 
3.8%
0 205
 
3.5%
1199.998 166
 
2.8%
30 161
 
2.7%
16 150
 
2.5%
Other values (220) 3003
51.0%
ValueCountFrequency (%)
0 205
3.5%
0.003 6
 
0.1%
0.005 1
 
< 0.1%
0.007 1
 
< 0.1%
0.01 37
 
0.6%
0.018 3
 
0.1%
0.02 17
 
0.3%
0.03 8
 
0.1%
0.04 6
 
0.1%
0.048 1
 
< 0.1%
ValueCountFrequency (%)
3120.002 1
 
< 0.1%
1800.003 1
 
< 0.1%
1800.001 13
 
0.2%
1800 1
 
< 0.1%
1799.999 2
 
< 0.1%
1799.996 48
0.8%
1610.001 1
 
< 0.1%
1500.001 16
 
0.3%
1455.001 1
 
< 0.1%
1200.001 8
 
0.1%

filter_lambda_min
Real number (ℝ)

High correlation  Missing 

Distinct16
Distinct (%)1.4%
Missing4764
Missing (%)81.0%
Infinite0
Infinite (%)0.0%
Mean1917.4487
Minimum490
Maximum3488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2025-04-05T14:36:18.812650image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum490
5-th percentile1182
Q11480
median2086
Q32086
95-th percentile3488
Maximum3488
Range2998
Interquartile range (IQR)606

Descriptive statistics

Standard deviation560.97482
Coefficient of variation (CV)0.29256314
Kurtosis2.3753966
Mean1917.4487
Median Absolute Deviation (MAD)0
Skewness0.95835936
Sum2145625.1
Variance314692.75
MonotonicityNot monotonic
2025-04-05T14:36:18.933862image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2086 591
 
10.0%
1480 297
 
5.0%
3488 78
 
1.3%
1495 56
 
1.0%
1505 20
 
0.3%
2025 18
 
0.3%
653 14
 
0.2%
617.55 13
 
0.2%
1103 12
 
0.2%
720 6
 
0.1%
Other values (6) 14
 
0.2%
(Missing) 4764
81.0%
ValueCountFrequency (%)
490 1
 
< 0.1%
570 1
 
< 0.1%
617.55 13
 
0.2%
653 14
 
0.2%
720 6
 
0.1%
721 2
 
< 0.1%
1103 12
 
0.2%
1160 4
 
0.1%
1182 4
 
0.1%
1480 297
5.0%
ValueCountFrequency (%)
3488 78
 
1.3%
2154.5 2
 
< 0.1%
2086 591
10.0%
2025 18
 
0.3%
1505 20
 
0.3%
1495 56
 
1.0%
1480 297
5.0%
1182 4
 
0.1%
1160 4
 
0.1%
1103 12
 
0.2%

filter_lambda_max
Real number (ℝ)

High correlation  Missing 

Distinct16
Distinct (%)1.4%
Missing4764
Missing (%)81.0%
Infinite0
Infinite (%)0.0%
Mean2080.9752
Minimum590
Maximum4115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2025-04-05T14:36:19.045384image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum590
5-th percentile1392
Q11770
median2105
Q32105
95-th percentile4115
Maximum4115
Range3525
Interquartile range (IQR)335

Descriptive statistics

Standard deviation625.06886
Coefficient of variation (CV)0.30037305
Kurtosis5.6710595
Mean2080.9752
Median Absolute Deviation (MAD)0
Skewness1.9988855
Sum2328611.2
Variance390711.08
MonotonicityNot monotonic
2025-04-05T14:36:19.175907image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2105 591
 
10.0%
1770 297
 
5.0%
4115 78
 
1.3%
1825 56
 
1.0%
1801 20
 
0.3%
2299 18
 
0.3%
659 14
 
0.2%
674.25 13
 
0.2%
1392 12
 
0.2%
1030 6
 
0.1%
Other values (6) 14
 
0.2%
(Missing) 4764
81.0%
ValueCountFrequency (%)
590 1
 
< 0.1%
659 14
 
0.2%
674.25 13
 
0.2%
690 1
 
< 0.1%
868 2
 
< 0.1%
1030 6
 
0.1%
1192 4
 
0.1%
1320 4
 
0.1%
1392 12
 
0.2%
1770 297
5.0%
ValueCountFrequency (%)
4115 78
 
1.3%
2299 18
 
0.3%
2177.5 2
 
< 0.1%
2105 591
10.0%
1825 56
 
1.0%
1801 20
 
0.3%
1770 297
5.0%
1392 12
 
0.2%
1320 4
 
0.1%
1192 4
 
0.1%

Filter
Text

Missing 

Distinct56
Distinct (%)1.2%
Missing1171
Missing (%)19.9%
Memory size300.1 KiB
2025-04-05T14:36:19.396690image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length16
Median length14
Mean length8.2296265
Min length1

Characters and Unicode

Total characters38 778
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowOPEN,NB2090
2nd rowOPEN,NB2090
3rd rowOPEN,NB2090
4th rowOPEN,NB2090
5th rowOPEN,NB2090
ValueCountFrequency (%)
free,og590 1128
23.9%
open,nb2090 591
12.5%
free,shp700 487
10.3%
ks 408
 
8.7%
h 258
 
5.5%
b_h,d_h23 189
 
4.0%
sh,open 188
 
4.0%
free,her_5 134
 
2.8%
hx1e-3,yj 128
 
2.7%
hx5e-2 114
 
2.4%
Other values (46) 1087
23.1%
2025-04-05T14:36:19.764235image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 5175
13.3%
, 3635
 
9.4%
0 3617
 
9.3%
R 2246
 
5.8%
H 2188
 
5.6%
O 2158
 
5.6%
N 2077
 
5.4%
F 1967
 
5.1%
9 1719
 
4.4%
P 1659
 
4.3%
Other values (27) 12337
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 5175
13.3%
, 3635
 
9.4%
0 3617
 
9.3%
R 2246
 
5.8%
H 2188
 
5.6%
O 2158
 
5.6%
N 2077
 
5.4%
F 1967
 
5.1%
9 1719
 
4.4%
P 1659
 
4.3%
Other values (27) 12337
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 5175
13.3%
, 3635
 
9.4%
0 3617
 
9.3%
R 2246
 
5.8%
H 2188
 
5.6%
O 2158
 
5.6%
N 2077
 
5.4%
F 1967
 
5.1%
9 1719
 
4.4%
P 1659
 
4.3%
Other values (27) 12337
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 5175
13.3%
, 3635
 
9.4%
0 3617
 
9.3%
R 2246
 
5.8%
H 2188
 
5.6%
O 2158
 
5.6%
N 2077
 
5.4%
F 1967
 
5.1%
9 1719
 
4.4%
P 1659
 
4.3%
Other values (27) 12337
31.8%

MJD-OBS
Real number (ℝ)

High correlation 

Distinct5873
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56223.242
Minimum51632.211
Maximum60749.283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2025-04-05T14:36:19.956789image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum51632.211
5-th percentile52490.134
Q154900.285
median55703.077
Q357932.195
95-th percentile60040.14
Maximum60749.283
Range9117.0722
Interquartile range (IQR)3031.9097

Descriptive statistics

Standard deviation2256.0192
Coefficient of variation (CV)0.040126096
Kurtosis-0.82876811
Mean56223.242
Median Absolute Deviation (MAD)2129.1542
Skewness0.0023512414
Sum3.3076133 × 108
Variance5089622.7
MonotonicityNot monotonic
2025-04-05T14:36:20.120465image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57495.07388 2
 
< 0.1%
57495.07768 2
 
< 0.1%
58499.33401 2
 
< 0.1%
57495.07689 2
 
< 0.1%
57495.07454 2
 
< 0.1%
56672.28377 2
 
< 0.1%
57494.10165 2
 
< 0.1%
57495.07614 2
 
< 0.1%
57494.10255 2
 
< 0.1%
57495.07313 2
 
< 0.1%
Other values (5863) 5863
99.7%
ValueCountFrequency (%)
51632.21061 1
< 0.1%
51632.21074 1
< 0.1%
51632.22511 1
< 0.1%
51632.22524 1
< 0.1%
51632.23963 1
< 0.1%
51632.23976 1
< 0.1%
51634.21724 1
< 0.1%
51634.21738 1
< 0.1%
51634.22688 1
< 0.1%
51634.22701 1
< 0.1%
ValueCountFrequency (%)
60749.28283 1
< 0.1%
60749.28246 1
< 0.1%
60749.28014 1
< 0.1%
60749.28001 1
< 0.1%
60749.27756 1
< 0.1%
60749.27745 1
< 0.1%
60731.32136 1
< 0.1%
60731.32121 1
< 0.1%
60731.31736 1
< 0.1%
60731.3172 1
< 0.1%

Airmass
Real number (ℝ)

Missing 

Distinct965
Distinct (%)18.5%
Missing679
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean1.4577206
Minimum1.198
Maximum2.893
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2025-04-05T14:36:20.289199image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1.198
5-th percentile1.227
Q11.278
median1.332
Q31.517
95-th percentile2.10985
Maximum2.893
Range1.695
Interquartile range (IQR)0.239

Descriptive statistics

Standard deviation0.28850675
Coefficient of variation (CV)0.19791636
Kurtosis4.4203984
Mean1.4577206
Median Absolute Deviation (MAD)0.061
Skewness2.0860236
Sum7585.978
Variance0.083236147
MonotonicityNot monotonic
2025-04-05T14:36:20.429502image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.27 180
 
3.1%
1.271 173
 
2.9%
1.274 113
 
1.9%
1.272 105
 
1.8%
1.273 86
 
1.5%
1.275 69
 
1.2%
1.276 62
 
1.1%
1.278 59
 
1.0%
1.277 54
 
0.9%
1.279 48
 
0.8%
Other values (955) 4255
72.3%
(Missing) 679
 
11.5%
ValueCountFrequency (%)
1.198 23
0.4%
1.199 31
0.5%
1.2 22
0.4%
1.201 8
 
0.1%
1.202 17
0.3%
1.203 11
 
0.2%
1.204 16
0.3%
1.205 10
 
0.2%
1.206 4
 
0.1%
1.207 10
 
0.2%
ValueCountFrequency (%)
2.893 1
< 0.1%
2.886 1
< 0.1%
2.878 1
< 0.1%
2.871 1
< 0.1%
2.864 1
< 0.1%
2.856 1
< 0.1%
2.849 1
< 0.1%
2.841 1
< 0.1%
2.834 1
< 0.1%
2.827 1
< 0.1%

DIMM Seeing at Start
Real number (ℝ)

Missing 

Distinct216
Distinct (%)4.2%
Missing679
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean0.91103574
Minimum-1
Maximum3.49
Zeros0
Zeros (%)0.0%
Negative213
Negative (%)3.6%
Memory size46.1 KiB
2025-04-05T14:36:20.620020image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.34
Q10.6775
median0.89
Q31.2
95-th percentile1.74
Maximum3.49
Range4.49
Interquartile range (IQR)0.5225

Descriptive statistics

Standard deviation0.56925393
Coefficient of variation (CV)0.62484258
Kurtosis4.5888726
Mean0.91103574
Median Absolute Deviation (MAD)0.25
Skewness-0.76423732
Sum4741.03
Variance0.32405003
MonotonicityNot monotonic
2025-04-05T14:36:20.789893image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 213
 
3.6%
0.84 115
 
2.0%
0.81 78
 
1.3%
0.66 72
 
1.2%
0.88 71
 
1.2%
0.87 68
 
1.2%
1.03 67
 
1.1%
0.71 67
 
1.1%
0.76 66
 
1.1%
0.83 66
 
1.1%
Other values (206) 4321
73.4%
(Missing) 679
 
11.5%
ValueCountFrequency (%)
-1 213
3.6%
0.24 1
 
< 0.1%
0.26 4
 
0.1%
0.27 1
 
< 0.1%
0.28 4
 
0.1%
0.29 6
 
0.1%
0.3 12
 
0.2%
0.31 7
 
0.1%
0.32 6
 
0.1%
0.33 5
 
0.1%
ValueCountFrequency (%)
3.49 13
0.2%
2.92 1
 
< 0.1%
2.85 2
 
< 0.1%
2.76 2
 
< 0.1%
2.69 2
 
< 0.1%
2.66 1
 
< 0.1%
2.65 2
 
< 0.1%
2.64 2
 
< 0.1%
2.63 2
 
< 0.1%
2.62 1
 
< 0.1%

Interactions

2025-04-05T14:36:11.395761image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:04.958511image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:05.919122image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:06.827607image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:07.713181image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:08.619113image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:09.610972image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:10.561286image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:11.498986image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:05.095135image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:06.040359image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:06.997696image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:07.843903image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:08.731970image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:09.753792image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:10.670463image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:11.604939image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:05.228943image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:06.144878image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:07.111141image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:07.948926image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:08.852314image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:09.871447image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:10.771379image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:11.692310image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:05.338610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:06.275487image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:07.217683image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:08.046899image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:08.975719image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:09.983658image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:10.866437image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:11.797637image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:05.451751image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:06.379001image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:07.315316image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:08.148520image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:09.116816image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:10.141013image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:10.975049image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:12.198231image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:05.585002image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:06.491476image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:07.418854image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:08.283173image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:09.232492image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:10.277570image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:11.092360image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:12.311687image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:05.692649image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:06.599890image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:07.514367image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:08.399884image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:09.370191image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:10.361882image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:11.187019image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:12.420251image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:05.812561image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:06.721942image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:07.625639image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:08.514606image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:09.495758image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:10.464561image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:36:11.296692image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2025-04-05T14:36:20.897154image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
AirmassCategoryDECDIMM Seeing at StartExptimeInstrumentMJD-OBSModeOBJECTRATypefilter_lambda_maxfilter_lambda_min
Airmass1.0000.140-0.2580.294-0.1270.225-0.1410.2630.2370.0410.1360.3990.448
Category0.1401.0000.2890.1380.1880.6380.4080.8200.8390.2260.9330.4850.455
DEC-0.2580.2891.000-0.135-0.0980.5320.3040.4040.493-0.1040.368-0.618-0.671
DIMM Seeing at Start0.2940.138-0.1351.0000.0150.2100.0920.2220.284-0.1400.1880.1650.218
Exptime-0.1270.188-0.0980.0151.0000.3830.1500.4510.482-0.1440.6900.5510.591
Instrument0.2250.6380.5320.2100.3831.0000.5440.6310.7730.6420.7730.7330.773
MJD-OBS-0.1410.4080.3040.0920.1500.5441.0000.5730.688-0.8870.541-0.0350.015
Mode0.2630.8200.4040.2220.4510.6310.5731.0000.5910.5960.5580.6110.683
OBJECT0.2370.8390.4930.2840.4820.7730.6880.5911.0000.6910.7270.7580.839
RA0.0410.226-0.104-0.140-0.1440.642-0.8870.5960.6911.0000.532-0.405-0.473
Type0.1360.9330.3680.1880.6900.7730.5410.5580.7270.5321.0000.4460.484
filter_lambda_max0.3990.485-0.6180.1650.5510.733-0.0350.6110.758-0.4050.4461.0000.966
filter_lambda_min0.4480.455-0.6710.2180.5910.7730.0150.6830.839-0.4730.4840.9661.000

Missing values

2025-04-05T14:36:12.592955image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-05T14:36:12.996850image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-05T14:36:13.205578image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

OBJECTRADECProgram_IDInstrumentCategoryTypeModeDataset IDRelease_DateTPL IDTPL STARTExptimefilter_lambda_minfilter_lambda_maxFilterMJD-OBSAirmassDIMM Seeing at Start
0OBJECT217.252125-62.695083299.C-5022(A)HAWKIACQUISITIONOBJECTIMAGEHAWKI.2017-06-28T03:56:21.604Jun 28 2018HAWKI_img_acq_FastPhot2017-06-28T03:51:0216.7622086.02105.0OPEN,NB209057932.1641391.5331.08
1OBJECT217.252375-62.695167299.C-5022(A)HAWKIACQUISITIONOBJECTIMAGEHAWKI.2017-06-28T01:47:30.092Jun 28 2018HAWKI_img_acq_FastPhot2017-06-28T01:34:2416.7622086.02105.0OPEN,NB209057932.0746541.2950.95
2OBJECT217.252375-62.695167299.C-5022(A)HAWKIACQUISITIONOBJECTIMAGEHAWKI.2017-06-28T01:50:10.810Jun 28 2018HAWKI_img_acq_FastPhot2017-06-28T01:34:2416.7622086.02105.0OPEN,NB209057932.0765141.2970.96
3OBJECT217.252750-62.695222299.C-5022(A)HAWKIACQUISITIONOBJECTIMAGEHAWKI.2017-06-28T03:58:13.314Jun 28 2018HAWKI_img_acq_FastPhot2017-06-28T03:51:0216.7622086.02105.0OPEN,NB209057932.1654321.5391.22
4OBJECT217.252833-62.69522260.A-9800(L)HAWKIACQUISITIONOBJECTIMAGEHAWKI.2017-05-27T22:52:04.495May 27 2017HAWKI_img_acq_FastPhot2017-05-27T22:37:5816.7622086.02105.0OPEN,NB209057900.9528301.7120.64
5PROX CENTAURI217.252958-62.695278299.C-5022(A)HAWKISCIENCEOBJECTIMAGE,HITHAWKI.2017-06-28T05:15:11.757Jun 28 2018HAWKI_img_obs_FastPhot2017-06-28T04:01:5528.4072086.02105.0OPEN,NB209057932.2188861.8771.45
6PROX CENTAURI217.252958-62.695278299.C-5022(A)HAWKISCIENCEOBJECTIMAGE,HITHAWKI.2017-06-28T05:21:14.741Jun 28 2018HAWKI_img_obs_FastPhot2017-06-28T04:01:5528.4072086.02105.0OPEN,NB209057932.2230871.9140.95
7PROX CENTAURI217.252958-62.695278299.C-5022(A)HAWKISCIENCEOBJECTIMAGE,HITHAWKI.2017-06-28T05:22:45.633Jun 28 2018HAWKI_img_obs_FastPhot2017-06-28T04:01:5528.4072086.02105.0OPEN,NB209057932.2241391.9231.16
8PROX CENTAURI217.252958-62.695278299.C-5022(A)HAWKISCIENCEOBJECTIMAGE,HITHAWKI.2017-06-28T05:19:44.081Jun 28 2018HAWKI_img_obs_FastPhot2017-06-28T04:01:5528.4072086.02105.0OPEN,NB209057932.2220381.9041.31
9PROX CENTAURI217.252958-62.695278299.C-5022(A)HAWKISCIENCEOBJECTIMAGE,HITHAWKI.2017-06-28T05:19:13.860Jun 28 2018HAWKI_img_obs_FastPhot2017-06-28T04:01:5528.4072086.02105.0OPEN,NB209057932.2216881.9011.31
OBJECTRADECProgram_IDInstrumentCategoryTypeModeDataset IDRelease_DateTPL IDTPL STARTExptimefilter_lambda_minfilter_lambda_maxFilterMJD-OBSAirmassDIMM Seeing at Start
5873V645CEN217.433208-62.68052871.C-0026(A)EFOSC/1.8SCIENCENaNSPECTRUMEFOSC.2003-05-10T05:29:13.984May 9 2004EFOSC_spec_obs_Spectrum2003-05-10T05:29:049.998NaNNaNFREE52769.2286341.240-1.00
5874HEAR_SPEC217.433292-62.68052871.C-0026(A)EFOSC/1.8CALIBHEAR_SPECSPECTRUMEFOSC.2003-05-10T05:30:36.256May 10 2003EFOSC_spec_cal_Arcs2003-05-10T05:30:090.797NaNNaNFREE52769.2295861.241-1.00
5875HIP_70890217.433708-62.678750075.C-0100(A)SOFISCIENCEOTHERIMAGESOFI.2005-06-05T02:24:28.397Jun 5 2006SOFI_img_obs_AutoJitter2005-06-05T02:06:3360.0001505.01801.0H,OPEN53526.1003291.1981.74
5876HIP_70890217.433875-62.681472075.C-0100(A)SOFISCIENCEOTHERIMAGESOFI.2005-06-05T02:08:58.565Jun 5 2006SOFI_img_obs_AutoJitter2005-06-05T02:06:3360.0001505.01801.0H,OPEN53526.0895671.1981.70
5877HIP_70890217.434042-62.677083075.C-0100(A)SOFISCIENCEOTHERIMAGESOFI.2005-06-05T02:13:22.694Jun 5 2006SOFI_img_obs_AutoJitter2005-06-05T02:06:3360.0001505.01801.0H,OPEN53526.0926241.1981.70
5878HEAR_SPEC217.435000-62.67963971.C-0026(A)EFOSC/1.8CALIBHEAR_SPECSPECTRUMEFOSC.2003-05-11T06:44:08.275May 11 2003EFOSC_spec_cal_Arcs2003-05-11T06:43:410.794NaNNaNFREE52770.2806511.3600.97
5879V645CEN217.435000-62.67966771.C-0026(A)EFOSC/1.8SCIENCENaNSPECTRUMEFOSC.2003-05-11T06:43:05.184May 10 2004EFOSC_spec_obs_Spectrum2003-05-11T06:42:509.997NaNNaNFREE52770.2799211.3580.95
5880ESPRI_1429-6240217.436458-62.697833077.A-9009(A)FEROSSCIENCEOBJECT,WAVEECHELLEFEROS.2006-07-14T23:08:16.338Jul 14 2007FEROS_ech_obs_objcal2006-07-14T23:07:551799.999NaNNaNNaN53930.9640781.204-1.00
5881ESPRI_1429-6240217.439042-62.698194077.A-9009(A)FEROSSCIENCEOBJECT,SKYECHELLEFEROS.2006-07-14T23:39:31.223Jul 14 2007FEROS_ech_obs_objsky2006-07-14T23:39:101799.999NaNNaNNaN53930.9857781.198-1.00
5882SKY217.448875-62.670639087.C-0423(A)CRIRESACQUISITIONSKYIMAGECRIRE.2011-04-30T04:29:26.530Apr 30 2012CRIRES_spec_acq_NoAO2011-04-30T04:22:542.000NaNNaNKS55681.1871131.2710.88